Jinglan Li
Papers
1
Total Citations
22
H-Index
1
About
Jinglan Li is a leading researcher in estimation theory and sensor fusion, with a particular focus on adaptive filtering for mobile robotics and target tracking. Her major contributions lie in addressing the challenges posed by complex, real-world noise environments—specifically, the correlation between multiplicative and additive measurement noises. In her highly cited 2021 paper, "Adaptive cubature Kalman filter with the estimation of correlation between multiplicative noise and additive measurement noise," Li introduced a novel correlation multiplicative measurement noise model that significantly improves the accuracy of state estimation in mobile robot tracking tasks. This work, which has garnered 22 citations, provides a robust framework for handling non-Gaussian and dependent noise, a critical advancement for autonomous systems operating under uncertain conditions. Her research bridges theoretical rigor with practical application, offering solutions that enhance the reliability of navigation and perception in dynamic environments. Li’s work is essential reading for researchers and students in robotics, control systems, and signal processing, as it directly addresses the limitations of classical Kalman filters in real-world scenarios.
Research Focus
Key Achievements
Top Papers
- 1